This repo contains an implementation of model predictive contouring control (MPCC) based on the kinematic model of the car. Casadi framework is used for nonlinear mpcc problem formulation and Ipopt solver with Harwell Subroutines Library(HSL) are used to solve the nonlinear optimization problem. The controller is tested on two platforms: F1/10 platform and FSSIM simulator.
The mathematical formulation for Nonlinear Kinematic MPCC can be found here..
- Installation on Intel processors equipped PC.
pip install casadiInstall Ipopt from sources by following the instructions on https://github.com/casadi/casadi/wiki/InstallationLinux.
- Installation on Nvidia Jetson TX2. Head to https://github.com/casadi/casadi/wiki/InstallationLinux and install Casadi from source following their instructions. To avoid some build issues,install the following dependencies:
Install clang and llvm using:
sudo apt-get install libclang-dev
sudo apt-get install llvm-devFollow instructions to build IPOPT from source as well. Follow instructions on Adding Additional Linear Solvers section to use HSL libraries. Execute this while building casadi from source
cmake -DWITH_PYTHON=ON -DWITH_SUNDIALS=OFF -DWITH_CSPARSE=OFF -DWITH_TINYXML=OFF -DWITH_IPOPT=ON -DWITH_CLANG=ON ..- After everthing is set up,launch the F1/10 simulator if you want to test the node on the simulator.
roslaunch racecar_simulator simulator.launch- If the program is to be run on the actual F1/10 car, then launch particle filter on the car.
roslaunch nonlinear_mpc_casadi real_particle_filter.launchMake sure you are loading appropriate map files in this launch file.
- Launch the nonlinear MPCC node using appropriate launch file depending on whether you want to test on the simulator or actual F1/10 car.
roslaunch nonlinear_mpc_casadi mpcc_f110_car.launch ###FOR F1/10 car
roslaunch nonlinear_mpc_casadi mpcc_f110_simulator.launch ###FOR F1/10 simualtor Few sample clips of the MPCC in F1/10 simulator are shown below.
In the first one, MPCC is allowed to drive the car as fast as possible within the limits of parameters set on yaml file.
Here, MPCC is using only kinematic model of the car, but its full dynamic model can also be incorporated. The full dynamic model is not included in the repo here as it is part of the competition codebase.
A sample clip showing the full dynamic model with tyre forces is shown below:
FSSIM is a vehicle simulator dedicated for Formula Student Driverless Competition. It was developed for autonomous software testing purposes. You can find the installation and workspace setup instructions for the FSSIM interface in its repo here.
After you have setup the FSSIM simulator, you can run MPCC on the car by simply running its launch file as follows,
roslaunch nonlinear_mpc_casadi mpcc_fssim.launch Few sample clips of the MPCC in operation on FSSIM are shown below.
The parameters of the MPCC can be tuned in mpc_params_fssim.yaml file located in params folder inside the package.
- /(cmd_vel_topic_name) (ackermann_msgs/AckermannDriveStamped)
- for sending driving command to the car
- cmd_vel_topic_name: parameter in the mpc param file. (default: /car1_30m/vesc/high_level/ackermann_cmd_mux/input/nav_0)
- /mpc_trajectory (nav_msgs/Path)
- Trajectory computed by mpc
- /boundary_marker (visualization_msgs/MarkerArray)
- for visualizing path constraints
- /mpc_prediction (MPC_trajectory)
- detailed information of mpc trajectory for reachability computation
- /mpc_metadata (MPC_metadata)
- metadata of MPC for reachability computation
- /center_path (nav_msgs/Path)
- Reference centerline path of the track for MPCC tracking
- /left_path (nav_msgs/Path)
- Left boundary visualization for path constraints
- /right_path (nav_msgs/Path)
- Right boundary visualization for path constraints
- /obstacle_marker (visualization_msgs/MarkerArray)
- Elliptical marker for visualizing obstacles in the track
- /(goal_topic_name) (geometry_msgs/PoseStamped)
- the goal can be given from the rviz or through the program itself to initiate the planning
- goal_topic_name: parameter in the mpc param file. (default: /move_base_simple/goal)
- /(pose_topic_name) (geometry_msgs/PoseStamped)
- pose of the robot extracted from particle filter or simulator
- pose_topic_name: parameter in the mpc param file. (default: /car1_30m/pf/viz/inferred_pose)
- /(odom_topic_name) (nav_msgs/Odometry)
- robot odometry
- odom_topic_name: parameter in the mpc param file. (default: /car1_30m/vesc/odom)
- controller_freq
- main loop rate (default: 15 [Hz])
- car_frame
- robot's coordinate frame (default: /car1_30m/base_link)
- dT
- sampling time of MPC ( default: 0.1 [s])
- mpc_steps_N
- Number of steps in the MPC prediction horizon (default: 15)
- vehicle_L
- Wheelbase- distance betweeen front wheel and rear wheel (default:0.325 [m])
- car_width
- Width of the car (default: 0.3 [m]) -mpc_max_steering
- Maximum steering angle of the car(default: 0.418 [rad]) -mpc_max_throttle
- Maximum acceleration of the car(default: 1 [m/s^2])
- max_speed
- Maximum velocity of the car (default: 2 [m/s])
- path_folder_name
- Folder name of the track data to be referenced in mpc(default: lab_track)
- lag_time
- System lag time for executing controls(default: 0.1 [s])
- delay_mode
- Whether to include system lag time in mpc computation(default: true)
- debug_mode
- Display intermediate mpc calculations and timings(default: true)
- mpc_w_cte
- Weight for penalizing cross-track error (default: 100)
- mpc_w_lag
- Weight for penalizing lag error (default: 700)
- mpc_w_vel
- Weight for penalizing use of actuator (default: 1.5)
- mpc_w_p
- Weight for prioritizing progress(centerline travelling velocity) along the path (default: 45.0)
- mpc_w_delta
- Weight for penalizing use of steering actuator (default: 41)
- mpc_w_accel
- Weight for penalizing high acceleration behavior (default: 50)
- mpc_w_delta_d
- Weight for penalizing rate of change of steering angle (default: 1500)